Single-channel blind source separation empowered joint transceiver optimization for wireless communications using deep learning✩

Pengcheng Guo; Fuqiang Yao; Miao Yu; Cheng Li; Yanqun Tang; Zhaolong Ning

doi:10.1016/j.dcan.2025.04.008

›› 2026, Vol. 12 ›› Issue (1) :76 -85. DOI: 10.1016/j.dcan.2025.04.008

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Single-channel blind source separation empowered joint transceiver optimization for wireless communications using deep learning^✩

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^a The Sixty-third Research Institute, National University of Defense Technology, Nanjing 210007, China

^b State Key Laboratory of Complex Electromagnetic Environment Eﬀects on Electronics and Information System, Luoyang 471003, China

^c School of Electronics and Communication Engineering, Sun Yat-Sen University, Guangzhou, 510006, China

^d School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

^* E-mail addresses: gpc_21@nudt.edu.cn (P. Guo), yfq2030@163.com (F. Yao), yumiao17@nudt.edu.cn (M. Yu), licheng@nudt.edu.cn (C. Li), tangyq8@mail.sysu.edu.cn (Y. Tang), ningzl@cqupt.edu.cn (Z. Ning).

Pengcheng Guo received the B.S. and M.S. degrees from the College of Communica-tions Engineering, Army Engineering University of PLA, in 2008 and 2015, respectively, and the Ph.D. degree from the College of Electronic Science and Technology, National University of Defense Technology (NUDT), in 2025. His research interests include deep learning, blind source separation, and anti-jamming communications.

Fuqiang Yao received his M.S. and Ph.D. degrees in communication and electronic system from Xidian University, Xi’an, China, in 1990 and 1993, respectively. He is cur-rently a science researcher fellow with the Six-third Research Institute, National University of Defense Technology (NUDT), Nanjing, China. He is also an academician of the Chinese Academy of Engineering (CAE). His current research interest is in anti-jamming commu-nications.

Miao Yu received the M.S. and Ph.D degrees in information and communication engi-neering from Zhejiang University in 2005 and 2009 respectively. He is currently a senior engineer in the Sixty-third Research Institute, National University of Defense Technology (NUDT). His research interests include blind signal processing and wireless communica-tion.

Cheng Li received the B.S. degree in information engineering, the M.S. and Ph.D. de-gree in information and communication engineering from National University of Defense Technology, Changsha, in 2007, 2009 and 2015, respectively. He is currently an assis-tant research fellow of the Sixty-third Research Institute, National University of Defense Technology, Nanjing. His current research interests include signal processing, wireless communication and electromagnetic countermeasure.

Yanqun Tang received the B.Sc., M.Sc., and Ph.D. degrees from the School of Elec-tronic Science and Engineering, National University of Defense Technology, China, in 2007, 2009 and 2013 respectively. He is currently an associate processor at the School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen, China. His research interests are integrated sensing and communication, full duplex commu-nications, wireless physical layer security and machine learning techniques for wireless communications.

Zhaolong Ning received the PhD degree from Northeastern University, China, in 2014. Currently, he is a full professor with the School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China. His research interests include mobile edge computing, 6G networks, machine learning, and resource management. He has published more than 150 scientific papers in interna-tional journals and conferences. He is a Highly Cited Researcher (Web of Science) since 2020, and an IET Fellow.

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Abstract

To tackle the physical layer security challenges in wireless communication, this paper introduces a multi-user architecture that leverages single-channel blind source separation, centered around a Multi-source Signal Mixture Separator (MSMS). This architecture consists of a multi-user encoder, a channel layer, and a separation decoder, allowing it to handle multiple functions simultaneously, including encoding, modulation, signal separation, demodulation, and decoding. The MSMS receiver eﬀectively enables the separation of numerous user signals, making it exceedingly diﬃcult for unauthorized eavesdroppers to extract valuable information from the mixed signals, thus significantly enhancing communication security. The MSMS can address the challenges of few-shot sample training and achieve joint optimization during transmission by employing a deep learning-based network design. The design of a single receiver reduces system costs and improves spectrum eﬃciency. The MSMS outperforms traditional Space-time Block Coding (STBC) strategies regarding separation performance, particularly in Block Error Rate (BLER) metrics. Modulation constellation diagrams further analyze the eﬀectiveness of multi-source signal mixture separation. Moreover, this study extends the MSMS framework from a two-user scenario to a three-user scenario, further demonstrating the flexibility and scalability of the proposed architecture.

Keywords

Physical layer security / Multi-user wireless communication / Single-channel source separation / Deep learning / Space-time block coding

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Pengcheng Guo, Fuqiang Yao, Miao Yu, Cheng Li, Yanqun Tang, Zhaolong Ning. Single-channel blind source separation empowered joint transceiver optimization for wireless communications using deep learning^✩. , 2026, 12(1): 76-85 DOI:10.1016/j.dcan.2025.04.008

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CRediT authorship contribution statement

Pengcheng Guo: Writing-original draft. Fuqiang Yao: Writing-review & editing, Methodology. Miao Yu: Writing-review & editing. Cheng Li: Writing-review & editing, Methodology. Yanqun Tang: Writing-review & editing. Zhaolong Ning: Writing-review & editing.

Declaration of competing interest

No potential conflict of interest was reported by the authors.

Acknowledgements

This work was supported by the National Social Science Foundation of China under Grant 2022-SKJJ-B-112. The authors thank AiMi Aca-demic Services (www.aimieditor.com) for English language editing and review services.

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